NEXUS — Resilient Intelligent Middleware

Service-oriented computing is the latest software paradigm brought about by the computing revolution. The different viewpoint and granularity of software components used in technologies based on the service-oriented architecture paradigm, compared to traditional object-oriented methodologies makes them better suited to many of the efforts that are being made in the pervasive and ubiquitous computing world. In the NEXUS project we are seeking ways of fusing the SOA and pervasive computing paradigms in order to build intelligent, robust and resilient networks connecting dynamic islands of service resources.     

Eigensolution of specially structured matrices with hyper‐symmetry

Recently, four canonical forms have been developed and applied to the dynamics and stability analysis of symmetric frames. In this paper, hyper‐symmetric matrices and specially structured matrices are defined and efficient methods are proposed for the eigensolution of such matrices. Applications are extended to hyper‐graphs and specially structured graphs. Simple methods are developed for calculating the eigenvalues of the Laplacian matrices of such graphs. The developments presented in this paper can also be considered as generalization of Form II and Form III symmetry, previously defined by the authors. Copyright © 2006 John Wiley & Sons, Ltd.     

A combinatorial study of the rigidity of planar structures

Summary A combinatorial method is presented for examining the rigidity of planar structures. In this approach, an expansion process is used for the formation of a statically determinate substructure, known as a -tree of a structure. The algorithm of Lováz and Yemini, and the method of Sugihara are employed for the recognition of the elementary subgraphs, during this process.With 4 Figures     

A comparative study of two approaches for supporting optimal network location queries

Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald’s) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L₁ and L₂/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this article, we focus on optimal network location (ONL) queries, i.e., optimal location queries in which objects and sites reside on a spatial network. We introduce two complementary approaches, namely EONL (short for Expansion-based ONL) and BONL (short for Bound-based ONL), which enable efficient computation of ONL queries with datasets of uniform and skewed distributions, respectively. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approaches with real world datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.     

Efficient indexing and retrieval of large-scale geo-tagged video databases

We are witnessing a significant growth in the number of smartphone users and advances in phone hardware and sensor technology. In conjunction with the popularity of video applications such as YouTube, an unprecedented number of user-generated videos (UGVs) are being generated and consumed by the public, which leads to a Big Data challenge in social media. In a very large video repository, it is difficult to index and search videos in their unstructured form. However, due to recent development, videos can be geo-tagged (e.g., locations from GPS receiver and viewing directions from digital compass) at the acquisition time, which can provide potential for efficient management of video data. Ideally, each video frame can be tagged by the spatial extent of its coverage area, termed Field-Of-View (FOV). This effectively converts a challenging video management problem into a spatial database problem. This paper attacks the challenges of large-scale video data management using spatial indexing and querying of FOVs, especially maximally harnessing the geographical properties of FOVs. Since FOVs are shaped similar to slices of pie and contain both location and orientation information, conventional spatial indexes, such as R-tree, cannot index them efficiently. The distribution of UGVs’ locations is non-uniform (e.g., more FOVs in popular locations). Consequently, even multilevel grid-based indexes, which can handle both location and orientation, have limitations in managing the skewed distribution. Additionally, since UGVs are usually captured in a casual way with diverse setups and movements, no a priori assumption can be made to condense them in an index structure. To overcome the challenges, we propose a class of new R-tree-based index structures that effectively harness FOVs’ camera locations, orientations and view-distances, in tandem, for both filtering and optimization. We also present novel search strategies and algorithms for efficient range and directional queries on our indexes. Our experiments using both real-world and large synthetic video datasets (over 30 years’ worth of videos) demonstrate the scalability and efficiency of our proposed indexes and search algorithms.     

ProDA: An End-to-End Wavelet-Based OLAP System for Massive Datasets

ProDA employs wavelets to support exact, approximate, and progressive OLAP queries on large multidimensional datasets, while keeping update costs relatively low. ProDA not only supports online execution of ad hoc analytical queries on massive datasets, but also extends the set of supported analytical queries to include the entire family of polynomial aggregate queries as well as the new class of plot queries.     

A hybrid aggregation and compression technique for road network databases

Vector data and in particular road networks are being queried, hosted and processed in many application domains such as in mobile computing. Many client systems such as PDAs would prefer to receive the query results in unrasterized format without introducing an overhead on overall system performance and result size. While several general vector data compression schemes have been studied by different communities, we propose a novel approach in vector data compression which is easily integrated within a geospatial query processing system. It uses line aggregation to reduce the number of relevant tuples and Huffman compression to achieve a multi-resolution compressed representation of a road network database. Our experiments performed on an end-to-end prototype verify that our approach exhibits fast query processing on both client and server sides as well as high compression ratio.

Freestanding hierarchical porous carbon film derived from hybrid nanocellulose for high-power supercapacitors

Nanocellulose is a sustainable and eco-friendly nanomaterial derived from renewable biomass.In this study,we utilized the structural advantages of two types of nanocellulose and fabricated freestanding carbonized hybrid nanocellulose films as electrode materials for supercapacitors.The long cellulose nanofibrils (CNFs) formed a macroporous framework,and the short cellulose nanocrystals were assembled around the CNF framework and generated micro/mesopores.This two-level hierarchical porous structure was successfully preserved during carbonization because of a thin atomic layer deposited (ALD) Al2O3 conformal coating,which effectively prevented the aggregation of nanocellulose.These carbonized,partially graphitized nanocellulose fibers were interconnected,forming an integrated and highly conductive network with a large specific surface area of 1,244 m2·g-1.The two-level hierarchical porous structure facilitated fast ion transport in the film.When tested as an electrode material with a high mass loading of 4 mg·cm-2 for supercapacitors,the hierarchical porous carbon film derived from hybrid nanocellulose exhibited a specific capacitance of 170 F.g-1and extraordinary performance at high current densities.Even at a very high current of 50 A·g-1,it retained 65％ of its original specific capacitance,which makes it a promising electrode material for high-power applications.     

Geometry and topology optimization of geodesic domes using charged system search

Dome structures provide cost-effective solutions for covering large areas without intermediate supports. In this article, simple procedures are developed to reach the configuration of the geodesic domes. A new definition of dome optimization problems is given which consists of finding optimal sections for elements (size optimization), optimal height for the crown (geometry optimization) and the optimum number of elements (topology optimization) under determined loading conditions. In order to find the optimum design, the recently developed meta-heuristic algorithm, known as the Charged System Search (CSS), is applied to the optimum design of geodesic domes. The CSS takes into account the nonlinear response of the domes. Using CSS, the optimum design of the geodesic domes is efficiently performed.